A Smart Biological Signal-Responsive Focal Drug Delivery System for Treatment of Refractory Epilepsy
Bibliographic record
Abstract
In this paper, we propose a new biological signal-responsive implantable device that triggers direct an anticonvulsive drug into the epileptogenic zone at electrographic seizure onset. We describe the high-performance seizure-onset detection algorithm, low-power circuit technique and focal drug delivery system. The implantable device is composed of a preamplifier, a signal processor, a seizure detector and a micropump. The device records high quality intracerebral electroencephalographic (icEEG) signals using high conductive electrodes and a low noise preamplifier. The recorded signal is processed continuously using low-power technique to detect onset of seizures accurately. The low-power miniaturized micropump is able to deliver sufficient amount of anticonvulsive drug in a short duration (50µL/sec) to epileptogenic zone. The detection algorithm was validated with Matlab tools and a prototype device was assembled with discrete components in a circular (Ø 40 mm) printed circuit board. The device was validated offline using the icEEG recordings obtained from 3 drug-resistant epilepsy patients. The average seizure detection delay was 10 sec from electrographic seizure onset, well before seizure progression to adjacent functional cortex.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".